A survey of multiple classifier systems as hybrid systems

By Michal Wozniak, Manuel Graña, Emilio Corchado. Information Fusion, 2014

959 citations recorded by the source corpus.

A current focus of intense research in pattern classification is the combination of several classifier systems, which can be built following either the same or different models and/or datasets building approaches. These systems perform information fusion of classification decisions at different levels overcoming limitations of traditional approaches based on single classifiers. This paper presents an up-to-date survey on multiple classifier system (MCS) from the point of view of Hybrid Intelligent Systems. The article discusses major issues, such as diversity and decision fusion methods, providing a vision of the spectrum of applications that are currently being developed.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/8acdf4a1e88b443b917d80e97850af7bbb1e3534

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Citations in the corpus

A review on machine learning principles for multi-view biological data integration (2016, methodology): “More elegant combination methods are discussed in [59].”
Big/little deep neural network for ultra low power inference (2015, background): “In the area of deep learning architectures, there are several approaches that utilize multiple neural networks in a hierarchical, coarse-to-fine, ensemble or cascade construction of neural networks [6,16,17,33].”
Imbalanced Deep Learning by Minority Class Incremental Rectification (2018, neutral): “(3) Hybrid: Combined data-level and algorithm-level rebalancing [55,56,57].”
Financial credit risk assessment: a recent review (2015, methodology): “The binary classifiers are then combined through pairwise coupling, max–min rule or weighted voting (Wozniaka et al. 2014).”
Continual Learning in Deep Neural Network by Using a Kalman Optimiser (2019, methodology): “To solve this problem, ensemble learning is among the solutions that are proposed in the existing work (Woźniak et al., 2014; Polikar et al., 2001; Dai et al., 2007).”
SVM and SVM Ensembles in Breast Cancer Prediction (2017, background): “Classifier ensembles combining multiple classifiers has come to be regarded as an important pattern classification technique [27 – 29] offering improved classification performance of a single classifier [16].”
Stock trend forecasting in turbulent market periods using neuro-fuzzy systems (2015, background): “Hybrid intelligent systems (Woz´niak et al. 2014) offer many alternatives for unorthodox handling of complex problems.”